When developing large language model (LLM) applications, data preparation is far and away the most crucial and usually overlooked stage in the development process. Training data quality, its structure, and alignment are the most crucial factors to ensure model performance. Without spending the proper time at the outset to get the data preparation right, leads to poor workflows, prolonged time to develop the application, and develop models which do not meet your performance expectations.
In this workshop, participants will work with real-world problems pertaining to issues like duplication, variability, and noise in big data. Using the open-source Data Prep Kit (DPK), we will transform data by showing you how to clean, deduplicate, and structure data for LLM tasks, including how to build a Retrieval-Augmented Generation (RAG) chatbot. Each participant will leave the workshop with tangible experience and reusable workflows to accelerate development opportunities and better outcomes.